An Ontological Representation of Documents and Queries for Information Retrieval Systems

Dragoni, Mauro; Pereira, Célia da Costa; Tettamanzi, Andrea G. B.

doi:10.1007/978-3-642-13025-0_57

Cited by 15 publications

(12 citation statements)

References 16 publications

(13 reference statements)

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“…For a concept C its frequency in document depends on the frequency of the term itself [40]. It's a ratio which is calculated as follows:…”

Section: ) Concept Weightingmentioning

confidence: 99%

A Semantic Analysis of the Learner’s Disorientation

Adda¹,

Bousbia²,

Balla³

2016

Int. J. Emerg. Technol. Learn.

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Abstract-Learning systems are dedicated for learning about a particular area organized through non-linear documents. Therefore, it is always useful to recognize the state of knowledge and the navigation behaviour of a learner in order to evaluate customize and adapt the learning process. In this paper, we aim to make a semantic analysis of the learner's navigation during his apprenticeship in hypermedia content. The main reason of this analysis is to identify the browsing behaviour of a learner with the current course. We assume that if the semantic distance between the domain concepts of the documents (or pages) that follow each other in the navigation of a learner is great, then this reflects the unstructured navigation behaviour and interprets that the learner is disoriented. This type of behaviour could be due to the poor organization of the content and the bad structuring of the course. Indeed, this analysis will allow to the tutor, to identify the disoriented learners and help them, and to the course author, to specify the causes to restructure and to deepen the analysis of the existing content and the navigation links between the parts of the course.

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“…For a concept C its frequency in document depends on the frequency of the term itself [40]. It's a ratio which is calculated as follows:…”

Section: ) Concept Weightingmentioning

confidence: 99%

A Semantic Analysis of the Learner’s Disorientation

Adda¹,

Bousbia²,

Balla³

2016

Int. J. Emerg. Technol. Learn.

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“…Conceptual IRSs are based on the assumption that document contents are better described by conceptual abstractions of real word entities than by lexical relationships that may be found within it or dictionaries [3] [11]. A cognitive view of the world is thus considered in such systems.…”

Section: Conceptual Irssmentioning

confidence: 99%

How Ontology Based Information Retrieval Systems May Benefit from Lexical Text Analysis

Ranwez

Duthil

et al. 2012

New Trends of Research in Ontologies and Lexical Resources

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“…Conceptual information retrieval systems (IRS) are based on the assumption that the contents of the document are better represented by conceptual abstractions of real word entities rather than by lexical relationships, that may be found within it or in dictionaries (Baziz et al, 2007;Dragoni et al, 2010). Looking at the semantic content of the document from another angle is the interpretation of the concepts of the documents in the context of a background conceptual domain knowledge model, such as Ontologies.…”

Section: Introductionmentioning

confidence: 99%

Enhanced ontology-based indexing and searching

Thenmalar

Geetha

2014

Aslib Journal of Information Management

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts. Design/methodology/approach -In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query. Findings -The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourismspecific ontology. The comparison of search results based on the use of ontology "with and without query expansion" is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the con...

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An Ontological Representation of Documents and Queries for Information Retrieval Systems

Cited by 15 publications

References 16 publications

A Semantic Analysis of the Learner’s Disorientation

A Semantic Analysis of the Learner’s Disorientation

How Ontology Based Information Retrieval Systems May Benefit from Lexical Text Analysis

Enhanced ontology-based indexing and searching

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